A robust noise reduction technique for time resolved CT

Authors


Abstract

Purpose:

To develop a noise reduction method for time resolved CT data, especially those with significant patient motion.

Methods:

PArtial TEmporal Nonlocal (PATEN) means is a technique that uses the redundant information in time-resolved CT data to achieve noise reduction. In this method, partial temporal profiles are used to determine the similarity (or weight) between pixels, and the similarity search makes use of both spatial and temporal information, providing robustness to patient motion. The performance of the PATEN filter was qualitatively and quantitatively evaluated with nine cardiac CT patient data sets and five CT brain perfusion patient data sets. In cardiac CT, PATEN was applied to reduce noise primarily in the reduced-dose phases created with electrocardiographic (ECG) pulsing. CT number accuracy and noise reduction were evaluated in both full-dose phases and reduced-dose phases between filtered backprojection images and PATEN filtered images. In CT brain perfusion, simulated quarter dose data were obtained by adding noise to the raw data of a routine dose scan. PATEN was applied to the simulated low-dose images. Image noise, time-intensity profile accuracy, and perfusion parameter maps were compared among low-dose, low-dose+PATEN filter, and full-dose images. The noise reduction performance of PATEN was compared to a previously proposed noise reduction method, time-intensity profile similarity (TIPS) bilateral filtering.

Results:

In 4D cardiac CT, after PATEN filtering, the image noise in the reduced-dose phases was greatly reduced, making anatomical structures easier to identify. The mean decreases in noise values between the original and PATEN images were 11.0% and 53.8% for the full and reduced-dose phases of the cardiac cycle, respectively. TIPS could not achieve effective noise reduction. In CT brain perfusion, PATEN achieved a 55.8%–66.3% decrease in image noise in the low-dose images. The contrast to noise ratio in the axial images was increased and was comparable to the full-dose images. Differentiation of anatomical structure in the PATEN images and corresponding quantitative perfusion parameter maps were preferred by two neuroradiologists compared to the simulated low-dose and TIPS results. The mean perfusion parameters calculated from the PATEN images agreed with those determined from full-dose data to within 12% and 20% for normal and diseased regions.

Conclusions:

In ECG-gated cardiac CT, where the dose had already been reduced by a factor of 5 in the reduced-dose phases, PATEN achieved a 53.8% noise reduction, which decreased the noise level in the reduced-dose phases close to that of the full-dose phases. In CT brain perfusion, a fourfold dose reduction was demonstrated to be achievable by PATEN filtering, which improved quantitative perfusion analysis. PATEN can be used to effectively reduce image noise to improve image quality, even when significant motion occurred between temporal samples.

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